A Coordination Model for Distributed Personnel Planning Patrick De Causmaecker, Peter Demeester, Greet Vanden Berghe, Bart Verbeke

Vakgroep IT KaHo Sint-Lieven2 Introduction Dingo: Negotiation in distributed personnel scheduling Exchange of employees between departments that are understaffed using agent technology Suitable / adaptable for many real-world problems (dynamic settings)

Vakgroep IT KaHo Sint-Lieven3 Objective and approach Interviews with 11 companies Classification of personnel scheduling problems (based on scheduling difficulties)

Vakgroep IT KaHo Sint-Lieven4 Classification Permanence centred police, hospitals Mobility centred home health care, health and safety board Fluctuation centred distribution, employment agency fast food, call centres (from literature) Project centred consulting, software development

Vakgroep IT KaHo Sint-Lieven5 Generalisation Position in scheduling space: Personnel: have qualifications and preferences Duty/task: require employees with qualifications Time: shifts, periods, holidays, … Personnel-Time plane: contract, holidays (many legal constraints) Duties-Time plane: coverage per shift Personnel-Duties plane: qualifications

Vakgroep IT KaHo Sint-Lieven6 Simplification Personnel-Time Assign employees to each department Use a tabu search algorithm to find solution per department Distribution of personnel among duties One software agent for each duty (department!) Negotiation in exchange of personnel

Vakgroep IT KaHo Sint-Lieven7 Case study Distribution company, warehouse A lot of distribution involved Personnel are polyvalent: every employee has several qualifications Ideal test case! In our model: every task = department Every department = represented by agent How organise exchange of personnel among the departments? Coordination model for this exchange?

Vakgroep IT KaHo Sint-Lieven8 Coordination model Opt to use Contract Net Protocol Contracting? Two interchangeable roles: Manager defines sub problems & coordinates the whole problem contractor executes sub task (possibly using sub contractors) Bid process to find a solution Announce a task (manager) Evaluate task (contractors) Bid (contractors) Evaluate and grant bid (manager) Coordinate & evaluate the whole (manager)

Vakgroep IT KaHo Sint-Lieven9 CNP manager Announce a task contactors manager Submit a bid contractors manager contractors Grant contract

Vakgroep IT KaHo Sint-Lieven10 Contract Net Protocol (CNP) Pro’s Dynamic task allocation to reach better contracts Agent population is dynamic Load balancing emerges naturally from bidding process High fault tolerance Cons No conflict resolution Assumes passive, generous, honest agents Communication intensive, high network load

Vakgroep IT KaHo Sint-Lieven11 Coordination mechanism for distributed personnel planning Contract Net Protocol (actually 3 X CNP) Only exchange of personnel, no need for an agreement 3 kinds of agents OmbudsAgent (OA) Department Agent (DA) Employee Agent (EA)

Vakgroep IT KaHo Sint-Lieven12 Ombuds Agent Department Agent i CFP Every one sends most expensive cost + time slot. Result of local search algorithm (Cost, T) i Evaluate every proposed change and generate corresponding cost ACCEPT PROPOSAL REJECT PROPOSAL Involved Department Agents exchange personnel and adapt department timetable for that shift CNP CFP (Cost, T) i Only these agents that have done changes send their costs CNP Employee Agent j send timetable to every personnel member CFP: Q max, T max Cost ACCEPT PROPOSAL Every involved agent evaluates its own constraints and generates a cost REJECT PROPOSAL Take highest cost Send others a REJECT Cost i ACCEPT PROPOSAL REJECT PROPOSAL CFP: Q max, T max CNP If Cost i < threshold, then change is accepted Otherwise not Sends agents that have done changes a new CFP

Vakgroep IT KaHo Sint-Lieven13 Ombuds AgentDepartment Agent i CFP Every one sends most expensive cost + timeslot. Result of local search algorithm (Cost, T) i ACCEPT PROPOSAL REJECT PROPOSAL CNP Employee Agent j Send personal timetable to every member of personnel Choose highest cost Send others a REJECT OA sends CFP to all DA Every DA: starts local tabu search algorithm Every EA (belonging to the department) gets its initial timetable of DA Every DA sends highest cost + time slot OA: selects highest cost

Vakgroep IT KaHo Sint-Lieven14 Evaluate every proposed change and generate corresponding cost Involved Department Agents exchange personnel and adapt department timetable for that shift CFP (Cost, T) i Only these agents that have done changes send their costs CFP: Q max, T max Cost ACCEPT PROPOSAL Every involved agent evaluates its own constraints and generates a cost REJECT PROPOSAL Cost i ACCEPT PROPOSAL REJECT PROPOSAL CFP: Q max, T max If Costi < threshold, then change is accepted Otherwise not Sends agents that have done changes a new CFP CNP OmbudsAgentDepartment Agent i Employee Agent j

Vakgroep IT KaHo Sint-Lieven15 Coordination model OA sends question to all DA containing a shift and qualification Proposed change is evaluated by DA AND EA EA evaluates personal constraints + generates extra cost when department changes DA generates cost if there is under coverage DA sends lowest cost From all received costs OA chooses lowest cost Change is executed DA adapts personnel Chosen EA adapts work spot Starting all over again!!!

Vakgroep IT KaHo Sint-Lieven16 Implementation issues Jade: De facto agent environment for Java some facilities for CNP (Initiator & Responder) Although: still a lot of complex programming problems with a large number of agents Mozart Oz: multi-paradigm, distributed programming language functionality for agents

Vakgroep IT KaHo Sint-Lieven17 Comment We make no difference between departments, qualifications and tasks Make no use of employee agents during tabu search Communication between agents would be a bottleneck Employee agents are created after tabu search If lots of personnel: only create employee agents when they are needed

Vakgroep IT KaHo Sint-Lieven18 Future Jade? Mozart Oz Communication between tabu search algorithm (implemented in Java) and coordination model (implemented in Oz): XML-RPC Testing with real data!

Questions?